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adsb_cells.py
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adsb_cells.py
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#!/usr/bin/python3
import math
import pygeodesy
import multiprocessing
import pandas as pd
import matplotlib.pyplot as plt
import random
import statistics
import util.common as common
from reconstructor_cell import ReconstructorCell
def create():
# add height offsets for calculation
print("calculating adsb geometric height offsets")
ginterpolator = pygeodesy.GeoidPGM(common.egm96_filename)
common.cat021_df['geometric_height_correction_m'] = ginterpolator.height(
common.cat021_df['latitude'], common.cat021_df['longitude'])
print("inializing adsb reconstructor cells")
common.cat021_df["latitude_bins"] = pd.cut(
common.cat021_df["latitude"], common.latitude_bins, labels=range(len(common.latitude_bins) - 1))
common.cat021_df["longitude_bins"] = pd.cut(
common.cat021_df["longitude"], common.longitude_bins, labels=range(len(common.longitude_bins) - 1))
common.cat021_df["time_of_day_bins"] = pd.cut(
common.cat021_df["time_of_day"], common.time_of_day_bins, labels=range(len(common.time_of_day_bins) - 1))
# print(cat021_df["longitude_bins"])
adsb_reconst_cells = [[[ReconstructorCell(tod_bin, common.time_of_day_bins,
lat_bin, common.latitude_bins,
lon_bin, common.longitude_bins)
for lon_bin in range(len(common.longitude_bins))]
for lat_bin in range(len(common.latitude_bins))]
for tod_bin in range(len(common.time_of_day_bins))]
print("filling adsb reconstructor cells")
for cnt in range(common.cat021_df.shape[0]):
if cnt % 200000 == 0:
print('creating indexes {}'.format(cnt))
# data bins
lat_bin = common.cat021_df["latitude_bins"].iloc[cnt]
lon_bin = common.cat021_df["longitude_bins"].iloc[cnt]
tod_bin = common.cat021_df["time_of_day_bins"].iloc[cnt]
if math.isnan(tod_bin):
print('tod_bin {} tod {}'.format(tod_bin, common.cat021_df["time_of_day"].iloc[cnt]))
continue
if math.isnan(lat_bin):
print('lat_bin {} lat {}'.format(lat_bin, common.cat021_df["latitude"].iloc[cnt]))
continue
if math.isnan(lon_bin):
print('lon_bin {} lon {}'.format(lon_bin, common.cat021_df["longitude"].iloc[cnt]))
continue
adsb_reconst_cells[tod_bin][lat_bin][lon_bin].indexes.append(cnt)
print('processing cells with {} tr'.format(common.cat021_df.shape[0]))
num_cells = 0
max_cell_size = None
# postprocess cells
for time_cells in adsb_reconst_cells:
for lat_index in range(len(time_cells)):
# for lat_cells in time_cells:
lat_cells = time_cells[lat_index]
pool = multiprocessing.Pool() # initialise your pool
finalized_cells = pool.map(ReconstructorCell.finalize, lat_cells)
pool.close() # shut down the pool
pool.join()
for cell in finalized_cells: # type: ReconstructorCell
num_cells += 1
if len(cell.indexes) > 0:
#cells.append(cell)
if max_cell_size is None or len(cell.indexes) > max_cell_size:
max_cell_size = len(cell.indexes)
# finalized_cells
time_cells[lat_index] = finalized_cells
print('cells {} avg size {} max size {}'.format(num_cells, common.cat021_df.shape[0] / num_cells, max_cell_size))
return adsb_reconst_cells
def calc_reconst_offsets(ratio):
num_misses = 0
offsets_equal = []
offsets_reconst = []
data_bins_len = len(common.cat021_df["time_of_day_bins"])
num_tests = int(data_bins_len * ratio)
test_indexes = random.sample(list(range(data_bins_len)), num_tests)
print('testing {} samples from {} target reports'.format(len(test_indexes), data_bins_len))
for test_index in test_indexes:
tod_bin = common.cat021_df["time_of_day_bins"].iloc[test_index]
lat_bin = common.cat021_df["latitude_bins"].iloc[test_index]
lon_bin = common.cat021_df["longitude_bins"].iloc[test_index]
# print('tod_bin {} lat_bin {} lon_bin {}'.format(tod_bin, lat_bin, lon_bin))
mc_ft = common.cat021_df['mode_c_code'].iloc[test_index]
geo_ft = common.cat021_df['geometric_height'].iloc[test_index]
assert not math.isnan(mc_ft)
assert not math.isnan(geo_ft)
cell = common.adsb_reconst_cells[tod_bin][lat_bin][lon_bin] # type: ReconstructorCell
if not cell.param_estimated:
num_misses += 1
continue
alt_cor = cell.calc_alt_corr(mc_ft)
offsets_equal.append(abs(geo_ft - mc_ft) * common.FT2M)
offsets_reconst.append(abs(geo_ft - alt_cor) * common.FT2M)
total_tests = num_misses + len(offsets_reconst)
print('cell sizes tod {} lat/lon {}'.format(common.tod_step, common.geo_step))
print('test performed {} misses {} perc {}'.format(total_tests, num_misses, 100 * num_misses / total_tests))
print('test equal errors avg {} std.dev. {}'.format(
statistics.mean(offsets_equal), statistics.stdev(offsets_equal)))
print('test reconst errors avg {} std.dev. {}'.format(
statistics.mean(offsets_reconst), statistics.stdev(offsets_reconst)))
return num_misses, offsets_equal, offsets_reconst
def plot_reconst_errors(offsets_equal, offsets_reconst):
xmin = min(min(offsets_equal), min(offsets_reconst))
xmax = max(max(offsets_equal), max(offsets_reconst))
x_range = [xmin, xmax]
common.num_bins = 50
# all
plt.hist(offsets_equal, range=x_range, bins=common.num_bins, log=True, alpha=0.5, label='Geo-Baro', color='r')
plt.hist(offsets_reconst, range=x_range, bins=common.num_bins, log=True, alpha=0.5, label='Geo-Reconst', color='b')
plt.xlabel('Altitude Error [m]')
plt.ylabel("Count")
plt.legend(loc='upper right')
fig = plt.gcf()
fig.set_size_inches(common.plot_size_x, common.plot_size_y)
plt.savefig(common.output_folder + '/' + 'reconst_errors_histo.png', dpi=common.plot_dpi, bbox_inches="tight")
plt.close()